Wavelet transform is an efficient tool for image compression, Wavelet transform provides multi-resolution image decomposition. which can be exploited via vector quantization to achieve a high compression ratio. For vector quantization of wavelet coefficients, the vectors consist of coefficients at the same level, different position, or different level, same position. This article compares the two methods and shows that, due to wavelet properties, vector quantization can still improve compression results by encoding only important vectors for reconstruction. Therefore, by prioritizing important vectors, you can achieve higher compression with better quality. The algorithm is also useful for embedded vector quantization coding of wavelet coefficients. INTRODUCTION Image compression reduces the data for image representation. Image compression techniques are divided into two main categories: Transformation techniques and Non-transformation techniques. Transformation techniques, compression is achieved by encoding the transformed coefficients. Wavelet transform-based compression decomposes the image into four subbands. These subbands represent the coarse and fine resolution image in different orientations. [l]For lossy coding of the coefficients, scalar or vector quantization can be used. In vector quantization, groups of coefficients are considered instead of individual pixels. The closest match is found for the provided code vector ID found by the codebook using some criteria. This provides a higher compression ratio. For wavelet coefficients, several vector quantization methods are suggested [2]. For all these methods, vector formation is considered in two ways. Intraband -, which groups pixels at the same level and in the same position. 2. Inter-band grouping pixels......center of paper......(1996) Jason Knipe, Xiaobo Li, Bin Han ''An improved lattice vector quantization scheme for wavelet compression” IEEE Transaction on Signal ProcessingVol. 46 no. Ipp 239-243 January I998,,45.6.Young Huh, 1.1 Hwang , and KRRao “Block Wavelet Transform Coding of Images Using Classified Vector Quantization” IEEE Transaction on Circuits and Systems for Video Technology vo1.5 n.1 February 1995 pp. 63- 67Amir Averbuch, Danny Lrar Moshe Israeli Image compression using wavelet transform and multirebolution decomposition” IEEE Transaction on Image Processing vo1.5 n.1 January 1996 pp. 4-12Madhuri Khambete, Dr. Madhuri Joshi ''Quality criterion based adaptive vector quantization using Hosaka plot” tencon99 vol.. pp. - 754-756Original boat image METHOD1PSNR = 22.47 dbCnmnrecsion cltin 77 bird nixdPROPOSED METHODPSNR= 25.23 dbCompression ratio .22 blpixel